Evolution of enzymes in metabolism: a network perspective

Molecular evolution of gland cell types and chemical interactions in animals

Across the Metazoa, the emergence of new ecological interactions has been enabled by the repeated evolution of exocrine glands. Specialized glands have arisen recurrently and with great frequency, even in single genera or species, transforming how animals interact with their environment through trophic resource exploitation, pheromonal communication, chemical defense and parental care. The widespread convergent evolution of animal glands implies that exocrine secretory cells are a hotspot of metazoan cell type innovation. Each evolutionary origin of a novel gland involves a process of 'gland cell type assembly': the stitching together of unique biosynthesis pathways; coordinated changes in secretory systems to enable efficient chemical release; and transcriptional deployment of these machineries into cells constituting the gland. This molecular evolutionary process influences what types of compound a given species is capable of secreting, and, consequently, the kinds of ecological interactions that species can display. Here, we discuss what is known about the evolutionary assembly of gland cell types and propose a framework for how it may happen. We posit the existence of 'terminal selector' transcription factors that program gland function via regulatory recruitment of biosynthetic enzymes and secretory proteins. We suggest ancestral enzymes are initially co-opted into the novel gland, fostering pleiotropic conflict that drives enzyme duplication. This process has yielded the observed pattern of modular, gland-specific biosynthesis pathways optimized for manufacturing specific secretions. We anticipate that single-cell technologies and gene editing methods applicable in diverse species will transform the study of animal chemical interactions, revealing how gland cell types are assembled and functionally configured at a molecular level.

CoMetGeNe: mining conserved neighborhood patterns in metabolic and genomic contexts

Background

In systems biology, there is an acute need for integrative approaches in heterogeneous network mining in order to exploit the continuous flux of genomic data. Simultaneous analysis of the metabolic pathways and genomic context of a given species leads to the identification of patterns consisting in reaction chains catalyzed by products of neighboring genes. Similar such patterns across several species can reveal their mode of conservation throughout the tree of life.

Results

We present CoMetGeNe (COnserved METabolic and GEnomic NEighborhoods), a novel method that identifies metabolic and genomic patterns consisting in maximal trails of reactions being catalyzed by products of neighboring genes. Patterns determined by CoMetGeNe in one species are subsequently employed in order to reflect their degree of conservation across multiple prokaryotic species. These interspecies comparisons help to improve genome annotation and can reveal putative alternative metabolic routes as well as unexpected gene ordering occurrences.

Conclusions

CoMetGeNe is an exploratory tool at both the genomic and the metabolic levels, leading to insights into the conservation of functionally related clusters of neighboring enzyme-coding genes. The open-source CoMetGeNe pipeline is freely available at https://cometgene.lri.fr.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2542-2) contains supplementary material, which is available to authorized users.

Influence of pathway topology and functional class on the molecular evolution of human metabolic genes

Metabolic networks comprise thousands of enzymatic reactions functioning in a controlled manner and have been shaped by natural selection. Thanks to the genome data, the footprints of adaptive (positive) selection are detectable, and the strength of purifying selection can be measured. This has made possible to know where, in the metabolic network, adaptive selection has acted and where purifying selection is more or less strong and efficient. We have carried out a comprehensive molecular evolutionary study of all the genes involved in the human metabolism. We investigated the type and strength of the selective pressures that acted on the enzyme-coding genes belonging to metabolic pathways during the divergence of primates and rodents. Then, we related those selective pressures to the functional and topological characteristics of the pathways. We have used DNA sequences of all enzymes (956) of the metabolic pathways comprised in the HumanCyc database, using genome data for humans and five other mammalian species. We have found that the evolution of metabolic genes is primarily constrained by the layer of the metabolism in which the genes participate: while genes encoding enzymes of the inner core of metabolism are much conserved, those encoding enzymes participating in the outer layer, mediating the interaction with the environment, are evolutionarily less constrained and more plastic, having experienced faster functional evolution. Genes that have been targeted by adaptive selection are endowed by higher out-degree centralities than non-adaptive genes, while genes with high in-degree centralities are under stronger purifying selection. When the position along the pathway is considered, a funnel-like distribution of the strength of the purifying selection is found. Genes at bottom positions are highly preserved by purifying selection, whereas genes at top positions, catalyzing the first steps, are open to evolutionary changes. These results show how functional and topological characteristics of metabolic pathways contribute to shape the patterns of evolutionary pressures driven by natural selection and how pathway network structure matters in the evolutionary process that shapes the evolution of the system.

Molecular evolution of acetohydroxyacid synthase in bacteria

Acetohydroxyacid synthase (AHAS) is the key enzyme in the biosynthetic pathways of branched chain amino acids in bacteria. Since it does not exist in animal and plant cells, AHAS is an attractive target for developing antimicrobials and herbicides. In some bacteria, there is a single copy of AHAS, while in others there are multiple copies. Therefore, it is necessary to investigate the origin and evolutionary pathway of various AHASs in bacteria. In this study, all the available protein sequences of AHAS in bacteria were investigated, and an evolutionary model of AHAS in bacteria is proposed, according to gene structure, organization and phylogeny. Multiple copies of AHAS in some bacteria might be evolved from the single copy of AHAS, the ancestor. Gene duplication, domain deletion and horizontal gene transfer might occur during the evolution of this enzyme. The results show the biological significance of AHAS, help to understand the functions of various AHASs in bacteria, and would be useful for developing industrial production strains of branched chain amino acids or novel antimicrobials.

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.

A global evolutionary and metabolic analysis of human obesity gene risk variants

It is generally accepted that the selection of gene variants during human evolution optimized energy metabolism that now interacts with our obesogenic environment to increase the prevalence of obesity. The purpose of this study was to perform a global evolutionary and metabolic analysis of human obesity gene risk variants (110 human obesity genes with 127 nearest gene risk variants) identified using genome-wide association studies (GWAS) to enhance our knowledge of early and late genotypes. As a result of determining the mean frequency of these obesity gene risk variants in 13 available populations from around the world our results provide evidence for the early selection of ancestral risk variants (defined as selection before migration from Africa) and late selection of derived risk variants (defined as selection after migration from Africa). Our results also provide novel information for association of these obesity genes or encoded proteins with diverse metabolic pathways and other human diseases. The overall results indicate a significant differential evolutionary pattern for the selection of obesity gene ancestral and derived risk variants proposed to optimize energy metabolism in varying global environments and complex association with metabolic pathways and other human diseases. These results are consistent with obesity genes that encode proteins possessing a fundamental role in maintaining energy metabolism and survival during the course of human evolution.

Reconstruction of insulin signal flow from phosphoproteome and metabolome data

Cellular homeostasis is regulated by signals through multiple molecular networks that include protein phosphorylation and metabolites. However, where and when the signal flows through a network and regulates homeostasis has not been explored. We have developed a reconstruction method for the signal flow based on time-course phosphoproteome and metabolome data, using multiple databases, and have applied it to acute action of insulin, an important hormone for metabolic homeostasis. An insulin signal flows through a network, through signaling pathways that involve 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites. Analysis of the network reveals that insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, thereby controlling a key reaction in glycolysis. We thus provide a versatile method of reconstruction of signal flow through the network using phosphoproteome and metabolome data.

Establishment of quantitative severity evaluation model for spinal cord injury by metabolomic fingerprinting

Spinal cord injury (SCI) is a devastating event with a limited hope for recovery and represents an enormous public health issue. It is crucial to understand the disturbances in the metabolic network after SCI to identify injury mechanisms and opportunities for treatment intervention. Through plasma 1H-nuclear magnetic resonance (NMR) screening, we identified 15 metabolites that made up an "Eigen-metabolome" capable of distinguishing rats with severe SCI from healthy control rats. Forty enzymes regulated these 15 metabolites in the metabolic network. We also found that 16 metabolites regulated by 130 enzymes in the metabolic network impacted neurobehavioral recovery. Using the Eigen-metabolome, we established a linear discrimination model to cluster rats with severe and mild SCI and control rats into separate groups and identify the interactive relationships between metabolic biomarkers in the global metabolic network. We identified 10 clusters in the global metabolic network and defined them as distinct metabolic disturbance domains of SCI. Metabolic paths such as retinal, glycerophospholipid, arachidonic acid metabolism; NAD-NADPH conversion process, tyrosine metabolism, and cadaverine and putrescine metabolism were included. In summary, we presented a novel interdisciplinary method that integrates metabolomics and global metabolic network analysis to visualize metabolic network disturbances after SCI. Our study demonstrated the systems biological study paradigm that integration of 1H-NMR, metabolomics, and global metabolic network analysis is useful to visualize complex metabolic disturbances after severe SCI. Furthermore, our findings may provide a new quantitative injury severity evaluation model for clinical use.

Molecular evolution of threonine dehydratase in bacteria

Threonine dehydratase converts L-threonine to 2-ketobutyrate. Several threonine dehydratases exist in bacteria, but their origins and evolutionary pathway are unknown. Here we analyzed all the available threonine dehydratases in bacteria and proposed an evolutionary pathway leading to the genes encoding three different threonine dehydratases CTD, BTD1 and BTD2. The ancestral threonine dehydratase might contain only a catalytic domain, but one or two ACT-like subdomains were fused during the evolution, resulting BTD1 and BTD2, respectively. Horizontal gene transfer, gene fusion, gene duplication, and gene deletion may occur during the evolution of this enzyme. The results are important for understanding the functions of various threonine dehydratases found in bacteria.

Identifying reaction modules in metabolic pathways: bioinformatic deduction and experimental validation of a new putative route in purine catabolism

BACKGROUND

Enzymes belonging to mechanistically diverse superfamilies often display similar catalytic mechanisms. We previously observed such an association in the case of the cyclic amidohydrolase superfamily whose members play a role in related steps of purine and pyrimidine metabolic pathways. To establish a possible link between enzyme homology and chemical similarity, we investigated further the neighbouring steps in the respective pathways.

RESULTS

We identified that successive reactions of the purine and pyrimidine pathways display similar chemistry. These mechanistically-related reactions are often catalyzed by homologous enzymes. Detection of series of similar catalysis made by succeeding enzyme families suggested some modularity in the architecture of the central metabolism. Accordingly, we introduce the concept of a reaction module to define at least two successive steps catalyzed by homologous enzymes in pathways alignable by similar chemical reactions. Applying such a concept allowed us to propose new function for misannotated paralogues. In particular, we discovered a putative ureidoglycine carbamoyltransferase (UGTCase) activity. Finally, we present experimental data supporting the conclusion that this UGTCase is likely to be involved in a new route in purine catabolism.

CONCLUSIONS

Using the reaction module concept should be of great value. It will help us to trace how the primordial promiscuous enzymes were assembled progressively in functional modules, as the present pathways diverged from ancestral pathways to give birth to the present-day mechanistically diversified superfamilies. In addition, the concept allows the determination of the actual function of misannotated proteins.

Towards understanding how molecular networks evolve in plants

Residing beneath the phenotypic landscape of a plant are intricate and dynamic networks of genes and proteins. As evolution operates on phenotypes, we expect its forces to shape somehow these underlying molecular networks. In this review, we discuss progress being made to elucidate the nature of these forces and their impact on the composition and structure of molecular networks. We also outline current limitations and open questions facing the broader field of plant network analysis.

Topological biosignatures: large-scale structure of chemical networks from biology and astrochemistry

The chemical basis of life involves more than simply the presence of biological molecules; biochemical systems embody a complex network of reactions with characteristic topological features. At the same time, chemical complexity is also present in nonbiological contexts, inviting us to clarify the relationship between chemistry and life through comparative studies. This study examines chemical networks from biology (the metabolism of E. coli) and astronomy (gas-phase reactions in dark molecular clouds) to establish common topological features that may be generic for any complex chemical system, as well as clear differences that may be topological signatures of life. The biological and astrochemical networks exhibit different scaling behaviors, and the network motifs found in the two systems show similarities as well as significant differences. The PageRank algorithm was used to quantify the degree to which individual species act primarily as products or reactants; in the metabolic network, these two roles are nearly identical for most species, whereas the astrochemical network shows a clearer partitioning into reactants and products.

Origins of specificity and promiscuity in metabolic networks

How enzymes have evolved to their present form is linked to the question of how pathways emerged and evolved into extant metabolic networks. To investigate this mechanism, we have explored the chemical diversity present in a largely unbiased data set of catalytic reactions processed by modern enzymes across the tree of life. In order to get a quantitative estimate of enzyme chemical diversity, we measure enzyme multispecificity or promiscuity using the reaction molecular signatures. Our main finding is that reactions that are catalyzed by a highly specific enzyme are shared by poorly divergent species, suggesting a later emergence of this function during evolution. In contrast, reactions that are catalyzed by highly promiscuous enzymes are more likely to appear uniformly distributed across species in the tree of life. From a functional point of view, promiscuous enzymes are mainly involved in amino acid and lipid metabolisms, which might be associated with the earliest form of biochemical reactions. In this way, results presented in this paper might assist us with the identification of primeval promiscuous catalytic functions contributing to life's minimal metabolism.

Horizontal and vertical growth of S. cerevisiae metabolic network

BACKGROUND

The growth and development of a biological organism is reflected by its metabolic network, the evolution of which relies on the essential gene duplication mechanism. There are two current views about the evolution of metabolic networks. The retrograde model hypothesizes that a pathway evolves by recruiting novel enzymes in a direction opposite to the metabolic flow. The patchwork model is instead based on the assumption that the evolution is based on the exploitation of broad-specificity enzymes capable of catalysing a variety of metabolic reactions.

RESULTS

We analysed a well-studied unicellular eukaryotic organism, S. cerevisiae, and studied the effect of the removal of paralogous gene products on its metabolic network. Our results, obtained using different paralog and network definitions, show that, after an initial period when gene duplication was indeed instrumental in expanding the metabolic space, the latter reached an equilibrium and subsequent gene duplications were used as a source of more specialized enzymes rather than as a source of novel reactions. We also show that the switch between the two evolutionary strategies in S. cerevisiae can be dated to about 350 million years ago.

CONCLUSIONS

Our data, obtained through a novel analysis methodology, strongly supports the hypothesis that the patchwork model better explains the more recent evolution of the S. cerevisiae metabolic network. Interestingly, the effects of a patchwork strategy acting before the Euascomycete-Hemiascomycete divergence are still detectable today.

Structural correlations in bacterial metabolic networks

Background

Evolution of metabolism occurs through the acquisition and loss of genes whose products acts as enzymes in metabolic reactions, and from a presumably simple primordial metabolism the organisms living today have evolved complex and highly variable metabolisms. We have studied this phenomenon by comparing the metabolic networks of 134 bacterial species with known phylogenetic relationships, and by studying a neutral model of metabolic network evolution.

Results

We consider the 'union-network' of 134 bacterial metabolisms, and also the union of two smaller subsets of closely related species. Each reaction-node is tagged with the number of organisms it belongs to, which we denote organism degree (OD), a key concept in our study. Network analysis shows that common reactions are found at the centre of the network and that the average OD decreases as we move to the periphery. Nodes of the same OD are also more likely to be connected to each other compared to a random OD relabelling based on their occurrence in the real data. This trend persists up to a distance of around five reactions. A simple growth model of metabolic networks is used to investigate the biochemical constraints put on metabolic-network evolution. Despite this seemingly drastic simplification, a 'union-network' of a collection of unrelated model networks, free of any selective pressure, still exhibit similar structural features as their bacterial counterpart.

Conclusions

The OD distribution quantifies topological properties of the evolutionary history of bacterial metabolic networks, and lends additional support to the importance of horizontal gene transfer during bacterial metabolic evolution where new reactions are attached at the periphery of the network. The neutral model of metabolic network growth can reproduce the main features of real networks, but we observe that the real networks contain a smaller common core, while they are more similar at the periphery of the network. This suggests that natural selection and biochemical correlations can act both to diversify and to narrow down metabolic evolution.

microRNA evolution in a human transcription factor and microRNA regulatory network

BACKGROUND

microRNAs (miRNAs) are important cellular components. The understanding of their evolution is of critical importance for the understanding of their function. Although some specific evolutionary rules of miRNAs have been revealed, the rules of miRNA evolution in cellular networks remain largely unexplored. According to knowledge from protein-coding genes, the investigations of gene evolution in the context of biological networks often generate valuable observations that cannot be obtained by traditional approaches.

RESULTS

Here, we conducted the first systems-level analysis of miRNA evolution in a human transcription factor (TF)-miRNA regulatory network that describes the regulatory relations among TFs, miRNAs, and target genes. We found that the architectural structure of the network provides constraints and functional innovations for miRNA evolution and that miRNAs showed different and even opposite evolutionary patterns from TFs and other protein-coding genes. For example, miRNAs preferentially coevolved with their activators but not with their inhibitors. During transcription, rapidly evolving TFs frequently activated but rarely repressed miRNAs. In addition, conserved miRNAs tended to regulate rapidly evolving targets, and upstream miRNAs evolved more rapidly than downstream miRNAs.

CONCLUSIONS

In this study, we performed the first systems level analysis of miRNA evolution. The findings suggest that miRNAs have a unique evolution process and thus may have unique functions and roles in various biological processes and diseases. Additionally, the network presented here is the first TF-miRNA regulatory network, which will be a valuable platform of systems biology.

Evolution of biomolecular networks: lessons from metabolic and protein interactions

Despite only becoming popular at the beginning of this decade, biomolecular networks are now frameworks that facilitate many discoveries in molecular biology. The nodes of these networks are usually proteins (specifically enzymes in metabolic networks), whereas the links (or edges) are their interactions with other molecules. These networks are made up of protein-protein interactions or enzyme-enzyme interactions through shared metabolites in the case of metabolic networks. Evolutionary analysis has revealed that changes in the nodes and links in protein-protein interaction and metabolic networks are subject to different selection pressures owing to distinct topological features. However, many evolutionary constraints can be uncovered only if temporal and spatial aspects are included in the network analysis.

Trade-offs between efficiency and robustness in bacterial metabolic networks are associated with niche breadth

The relation between structure and function in biologic networks is a central point of systems biology research. Key functional features--notably, efficiency and robustness--are linked to the topologic structure of a network, and there appears to be a degree of trade-off between these features, i.e., simulation studies indicate that more efficient networks tend to be less robust. Here, we investigate this issue in metabolic networks from 105 lineages of bacteria having a wide range of ecologies. We take quantitative measurements on each network and integrate this network data with ecologic data using a phylogenetic comparative model. In this setting, we find that biologic conclusions obtained with classical phylogenetic comparative methods are sensitive to correlations between model covariates and phylogenetic branch length. To avoid this problem, we propose a revised statistical framework--hierarchical mixed-effect regression--to accommodate phylogenetic nonindependence. Using this approach, we show that the cartography of metabolic networks does indeed reflect a trade-off between efficiency and robustness. Furthermore, ecologic characteristics related to niche breadth are strong predictors of network shape. Given the broad variation in niche breadth seen among species, we predict that there is no universally optimal balance between efficiency and robustness in bacterial metabolic networks and, thus, no universally optimal network structure. These results highlight the biologic relevance of variation in network structure and the potential role of niche breadth in shaping metabolic strategies of efficiency and robustness.

The origin and evolution of modern metabolism

One fundamental goal of current research is to understand how complex biomolecular networks took the form that we observe today. Cellular metabolism is probably one of the most ancient biological networks and constitutes a good model system for the study of network evolution. While many evolutionary models have been proposed, a substantial body of work suggests metabolic pathways evolve fundamentally by recruitment, in which enzymes are drawn from close or distant regions of the network to perform novel chemistries or use different substrates. Here we review how structural and functional genomics has impacted our knowledge of evolution of modern metabolism and describe some approaches that merge evolutionary and structural genomics with advances in bioinformatics. These include mining the data on structure and function of enzymes for salient patterns of enzyme recruitment. Initial studies suggest modern metabolism originated in enzymes of nucleotide metabolism harboring the P-loop hydrolase fold, probably in pathways linked to the purine metabolic subnetwork. This gateway of recruitment gave rise to pathways related to the synthesis of nucleotides and cofactors for an ancient RNA world. Once the TIM beta/alpha-barrel fold architecture was discovered, it appears metabolic activities were recruited explosively giving rise to subnetworks related to carbohydrate and then amino acid metabolism. Remarkably, recruitment occurred in a layered system reminiscent of Morowitz's prebiotic shells, supporting the notion that modern metabolism represents a palimpsest of ancient metabolic chemistries.

Evolutionarily conserved substrate substructures for automated annotation of enzyme superfamilies

The evolution of enzymes affects how well a species can adapt to new environmental conditions. During enzyme evolution, certain aspects of molecular function are conserved while other aspects can vary. Aspects of function that are more difficult to change or that need to be reused in multiple contexts are often conserved, while those that vary may indicate functions that are more easily changed or that are no longer required. In analogy to the study of conservation patterns in enzyme sequences and structures, we have examined the patterns of conservation and variation in enzyme function by analyzing graph isomorphisms among enzyme substrates of a large number of enzyme superfamilies. This systematic analysis of substrate substructures establishes the conservation patterns that typify individual superfamilies. Specifically, we determined the chemical substructures that are conserved among all known substrates of a superfamily and the substructures that are reacting in these substrates and then examined the relationship between the two. Across the 42 superfamilies that were analyzed, substantial variation was found in how much of the conserved substructure is reacting, suggesting that superfamilies may not be easily grouped into discrete and separable categories. Instead, our results suggest that many superfamilies may need to be treated individually for analyses of evolution, function prediction, and guiding enzyme engineering strategies. Annotating superfamilies with these conserved and reacting substructure patterns provides information that is orthogonal to information provided by studies of conservation in superfamily sequences and structures, thereby improving the precision with which we can predict the functions of enzymes of unknown function and direct studies in enzyme engineering. Because the method is automated, it is suitable for large-scale characterization and comparison of fundamental functional capabilities of both characterized and uncharacterized enzyme superfamilies.

The hidden universal distribution of amino acid biosynthetic networks: a genomic perspective on their origins and evolution

A core of widely distributed network branches biosynthesizing at least 16 out of the 20 standard amino acids is predicted using comparative genomics.

Background

Twenty amino acids comprise the universal building blocks of proteins. However, their biosynthetic routes do not appear to be universal from an Escherichia coli-centric perspective. Nevertheless, it is necessary to understand their origin and evolution in a global context, that is, to include more 'model' species and alternative routes in order to do so. We use a comparative genomics approach to assess the origins and evolution of alternative amino acid biosynthetic network branches.

Results

By tracking the taxonomic distribution of amino acid biosynthetic enzymes, we predicted a core of widely distributed network branches biosynthesizing at least 16 out of the 20 standard amino acids, suggesting that this core occurred in ancient cells, before the separation of the three cellular domains of life. Additionally, we detail the distribution of two types of alternative branches to this core: analogs, enzymes that catalyze the same reaction (using the same metabolites) and belong to different superfamilies; and 'alternologs', herein defined as branches that, proceeding via different metabolites, converge to the same end product. We suggest that the origin of alternative branches is closely related to different environmental metabolite sources and life-styles among species.

Conclusion

The multi-organismal seed strategy employed in this work improves the precision of dating and determining evolutionary relationships among amino acid biosynthetic branches. This strategy could be extended to diverse metabolic routes and even other biological processes. Additionally, we introduce the concept of 'alternolog', which not only plays an important role in the relationships between structure and function in biological networks, but also, as shown here, has strong implications for their evolution, almost equal to paralogy and analogy.

Bootstrapping the energy flow in the beginning of life

This paper suggests that the energy flow on which all living structures depend only started up slowly, the low-energy, initial phase starting up a second, slightly more energetic phase, and so on. In this way, the build up of the energy flow follows a bootstrapping process similar to that found in the development of computers, the first generation making possible the calculations necessary for constructing the second one, etc. In the biogenetic upstart of an energy flow, non-metals in the lower periods of the Periodic Table of Elements would have constituted the most primitive systems, their operation being enhanced and later supplanted by elements in the higher periods that demand more energy. This bootstrapping process would put the development of the metabolisms based on the second period elements carbon, nitrogen and oxygen at the end of the evolutionary process rather than at, or even before, the biogenetic event.

A network perspective on the evolution of metabolism by gene duplication

BACKGROUND

Gene duplication followed by divergence is one of the main sources of metabolic versatility. The patchwork and stepwise models of metabolic evolution help us to understand these processes, but their assumptions are relatively simplistic. We used a network-based approach to determine the influence of metabolic constraints on the retention of duplicated genes.

RESULTS

We detected duplicated genes by looking for enzymes sharing homologous domains and uncovered an increased retention of duplicates for enzymes catalyzing consecutive reactions, as illustrated by the ligases acting in the biosynthesis of peptidoglycan. As a consequence, metabolic networks show a high retention of duplicates within functional modules, and we found a preferential biochemical coupling of reactions that partially explains this bias. A similar situation was found in enzyme-enzyme interaction networks, but not in interaction networks of non-enzymatic proteins or gene transcriptional regulatory networks, suggesting that the retention of duplicates results from the biochemical rules governing substrate-enzyme-product relationships. We confirmed a high retention of duplicates between chemically similar reactions, as illustrated by fatty-acid metabolism. The retention of duplicates between chemically dissimilar reactions is, however, also greater than expected by chance. Finally, we detected a significant retention of duplicates as groups, instead of single pairs.

CONCLUSION

Our results indicate that in silico modeling of the origin and evolution of metabolism is improved by the inclusion of specific functional constraints, such as the preferential biochemical coupling of reactions. We suggest that the stepwise and patchwork models are not independent of each other: in fact, the network perspective enables us to reconcile and combine these models.

Evolution after gene duplication: models, mechanisms, sequences, systems, and organisms

Gene duplication is postulated to have played a major role in the evolution of biological novelty. Here, gene duplication is examined across levels of biological organization in an attempt to create a unified picture of the mechanistic process by which gene duplication can have played a role in generating biodiversity. Neofunctionalization and subfunctionalization have been proposed as important processes driving the retention of duplicate genes. These models have foundations in population genetic theory, which is now being refined by explicit consideration of the structural constraints placed upon genes encoding proteins through physical chemistry. Further, such models can be examined in the context of comparative genomics, where an integration of gene-level evolution and species-level evolution allows an assessment of the frequency of duplication and the fate of duplicate genes. This process, of course, is dependent upon the biochemical role that duplicated genes play in biological systems, which is in turn dependent upon the mechanism of duplication: whole genome duplication involving a co-duplication of interacting partners vs. single gene duplication. Lastly, the role that these processes may have played in driving speciation is examined.

The identification of similarities between biological networks: application to the metabolome and interactome

The increasing interest in systems biology has resulted in extensive experimental data describing networks of interactions (or associations) between molecules in metabolism, protein-protein interactions and gene regulation. Comparative analysis of these networks is central to understanding biological systems. We report a novel method (PHUNKEE: Pairing subgrapHs Using NetworK Environment Equivalence) by which similar subgraphs in a pair of networks can be identified. Like other methods, PHUNKEE explicitly considers the graphical form of the data and allows for gaps. However, it is novel in that it includes information about the context of the subgraph within the adjacent network. We also explore a new approach to quantifying the statistical significance of matching subgraphs. We report similar subgraphs in metabolic pathways and in protein-protein interaction networks. The most similar metabolic subgraphs were generally found to occur in processes central to all life, such as purine, pyrimidine and amino acid metabolism. The most similar pairs of subgraphs found in the protein-protein interaction networks of Drosophila melanogaster and Saccharomyces cerevisiae also include central processes such as cell division but, interestingly, also include protein sub-networks involved in pre-mRNA processing. The inclusion of network context information in the comparison of protein interaction networks increased the number of similar subgraphs found consisting of proteins involved in the same functional process. This could have implications for the prediction of protein function.

Modeling for evolving biological networks with scale-free connectivity, hierarchical modularity, and disassortativity

We propose a growing network model that consists of two tunable mechanisms: growth by merging modules which are represented as complete graphs and a fitness-driven preferential attachment. Our model exhibits the three prominent statistical properties are widely shared in real biological networks, for example gene regulatory, protein-protein interaction, and metabolic networks. They retain three power law relationships, such as the power laws of degree distribution, clustering spectrum, and degree-degree correlation corresponding to scale-free connectivity, hierarchical modularity, and disassortativity, respectively. After making comparisons of these properties between model networks and biological networks, we confirmed that our model has inference potential for evolutionary processes of biological networks.

Comparison of protein interaction networks reveals species conservation and divergence

Background

Recent progresses in high-throughput proteomics have provided us with a first chance to characterize protein interaction networks (PINs), but also raised new challenges in interpreting the accumulating data.

Results

Motivated by the need of analyzing and interpreting the fast-growing data in the field of proteomics, we propose a comparative strategy to carry out global analysis of PINs. We compare two PINs by combining interaction topology and sequence similarity to identify conserved network substructures (CoNSs). Using this approach we perform twenty-one pairwise comparisons among the seven recently available PINs of E.coli, H.pylori, S.cerevisiae, C.elegans, D.melanogaster, M.musculus and H.sapiens. In spite of the incompleteness of data, PIN comparison discloses species conservation at the network level and the identified CoNSs are also functionally conserved and involve in basic cellular functions. We investigate the yeast CoNSs and find that many of them correspond to known complexes. We also find that different species harbor many conserved interaction regions that are topologically identical and these regions can constitute larger interaction regions that are topologically different but similar in framework. Based on the species-to-species difference in CoNSs, we infer potential species divergence. It seems that different species organize orthologs in similar but not necessarily the same topology to achieve similar or the same function. This attributes much to duplication and divergence of genes and their associated interactions. Finally, as the application of CoNSs, we predict 101 protein-protein interactions (PPIs), annotate 339 new protein functions and deduce 170 pairs of orthologs.

Conclusion

Our result demonstrates that the cross-species comparison strategy we adopt is powerful for the exploration of biological problems from the perspective of networks.

MANET: tracing evolution of protein architecture in metabolic networks

Background

Cellular metabolism can be characterized by networks of enzymatic reactions and transport processes capable of supporting cellular life. Our aim is to find evolutionary patterns and processes embedded in the architecture and function of modern metabolism, using information derived from structural genomics.

Description

The Molecular Ancestry Network (MANET) project traces evolution of protein architecture in biomolecular networks. We describe metabolic MANET, a database that links information in the Structural Classification of Proteins (SCOP), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and phylogenetic reconstructions depicting the evolution of protein fold architecture. Metabolic MANET literally 'paints' the ancestries of enzymes derived from rooted phylogenomic trees directly onto over one hundred metabolic subnetworks, enabling the study of evolutionary patterns at global and local levels. An initial analysis of painted subnetworks reveals widespread enzymatic recruitment and an early origin of amino acid metabolism.

Conclusion

MANET maps evolutionary relationships directly and globally onto biological networks, and can generate and test hypotheses related to evolution of metabolism. We anticipate its use in the study of other networks, such as signaling and other protein-protein interaction networks.

Leveraging enzyme structure-function relationships for functional inference and experimental design: the structure-function linkage database

The study of mechanistically diverse enzyme superfamilies-collections of enzymes that perform different overall reactions but share both a common fold and a distinct mechanistic step performed by key conserved residues-helps elucidate the structure-function relationships of enzymes. We have developed a resource, the structure-function linkage database (SFLD), to analyze these structure-function relationships. Unique to the SFLD is its hierarchical classification scheme based on linking the specific partial reactions (or other chemical capabilities) that are conserved at the superfamily, subgroup, and family levels with the conserved structural elements that mediate them. We present the results of analyses using the SFLD in correcting misannotations, guiding protein engineering experiments, and elucidating the function of recently solved enzyme structures from the structural genomics initiative. The SFLD is freely accessible at http://sfld.rbvi.ucsf.edu.

Directed evolution of metabolic pathways

The modification of cellular metabolism is of biotechnological and commercial significance because naturally occurring metabolic pathways are the source of diverse compounds used in fields ranging from medicine to bioremediation. Directed evolution is the experimental improvement of biocatalysts or cellular properties through iterative genetic diversification and selection procedures. The creation of novel metabolic functions without disrupting the balanced intracellular pool of metabolites is the primary challenge of pathway manipulation. The introduction of coordinated changes across multiple genetic elements, in conjunction with functional selection, presents an integrated approach for the modification of metabolism with benign physiological consequences. Directed evolution formats take advantage of the dynamic structures of genomes and genomic sub-structures and their ability to evolve in multiple directions in response to external stimuli. The elucidation, design and application of genome-restructuring mechanisms are key elements in the directed evolution of cellular metabolic pathways.

Adaptive evolution of bacterial metabolic networks by horizontal gene transfer

Numerous studies have considered the emergence of metabolic pathways, but the modes of recent evolution of metabolic networks are poorly understood. Here, we integrate comparative genomics with flux balance analysis to examine (i) the contribution of different genetic mechanisms to network growth in bacteria, (ii) the selective forces driving network evolution and (iii) the integration of new nodes into the network. Most changes to the metabolic network of Escherichia coli in the past 100 million years are due to horizontal gene transfer, with little contribution from gene duplicates. Networks grow by acquiring genes involved in the transport and catalysis of external nutrients, driven by adaptations to changing environments. Accordingly, horizontally transferred genes are integrated at the periphery of the network, whereas central parts remain evolutionarily stable. Genes encoding physiologically coupled reactions are often transferred together, frequently in operons. Thus, bacterial metabolic networks evolve by direct uptake of peripheral reactions in response to changed environments.

Preferential attachment in the evolution of metabolic networks

BACKGROUND

Many biological networks show some characteristics of scale-free networks. Scale-free networks can evolve through preferential attachment where new nodes are preferentially attached to well connected nodes. In networks which have evolved through preferential attachment older nodes should have a higher average connectivity than younger nodes. Here we have investigated preferential attachment in the context of metabolic networks.

RESULTS

The connectivities of the enzymes in the metabolic network of Escherichia coli were determined and representatives for these enzymes were located in 11 eukaryotes, 17 archaea and 46 bacteria. E. coli enzymes which have representatives in eukaryotes have a higher average connectivity while enzymes which are represented only in the prokaryotes, and especially the enzymes only present in betagamma-proteobacteria, have lower connectivities than expected by chance. Interestingly, the enzymes which have been proposed as candidates for horizontal gene transfer have a higher average connectivity than the other enzymes. Furthermore, It was found that new edges are added to the highly connected enzymes at a faster rate than to enzymes with low connectivities which is consistent with preferential attachment.

CONCLUSION

Here, we have found indications of preferential attachment in the metabolic network of E. coli. A possible biological explanation for preferential attachment growth of metabolic networks is that novel enzymes created through gene duplication maintain some of the compounds involved in the original reaction, throughout its future evolution. In addition, we found that enzymes which are candidates for horizontal gene transfer have a higher average connectivity than other enzymes. This indicates that while new enzymes are attached preferentially to highly connected enzymes, these highly connected enzymes have sometimes been introduced into the E. coli genome by horizontal gene transfer. We speculate that E. coli has adjusted its metabolic network to a changing environment by replacing the relatively central enzymes for better adapted orthologs from other prokaryotic species.

Inferring meaningful pathways in weighted metabolic networks

An approach is presented for computing meaningful pathways in the network of small molecule metabolism comprising the chemical reactions characterized in all organisms. The metabolic network is described as a weighted graph in which all the compounds are included, but each compound is assigned a weight equal to the number of reactions in which it participates. Path finding is performed in this graph by searching for one or more paths with lowest weight. Performance is evaluated systematically by computing paths between the first and last reactions in annotated metabolic pathways, and comparing the intermediate reactions in the computed pathways to those in the annotated ones. For the sake of comparison, paths are computed also in the un-weighted raw (all compounds and reactions) and filtered (highly connected pool metabolites removed) metabolic graphs, respectively. The correspondence between the computed and annotated pathways is very poor (<30%) in the raw graph; increasing to approximately 65% in the filtered graph; reaching approximately 85% in the weighted graph. Considering the best-matching path among the five lightest paths increases the correspondence to 92%, on average. We then show that the average distance between pairs of metabolites is significantly larger in the weighted graph than in the raw unfiltered graph, suggesting that the small-world properties previously reported for metabolic networks probably result from irrelevant shortcuts through pool metabolites. In addition, we provide evidence that the length of the shortest path in the weighted graph represents a valid measure of the "metabolic distance" between enzymes. We suggest that the success of our simplistic approach is rooted in the high degree of specificity of the reactions in metabolic pathways, presumably reflecting thermodynamic constraints operating in these pathways. We expect our approach to find useful applications in inferring metabolic pathways in newly sequenced genomes.

PROTEIN FAMILIES AND THEIR EVOLUTION—A STRUCTURAL PERSPECTIVE

We can now assign about two thirds of the sequences from completed genomes to as few as 1400 domain families for which structures are known and thus more ancient evolutionary relationships established. About 200 of these domain families are common to all kingdoms of life and account for nearly 50% of domain structure annotations in the genomes. Some of these domain families have been very extensively duplicated within a genome and combined with different domain partners giving rise to different multidomain proteins. The ways in which these domain combinations evolve tend to be specific to the organism so that less than 15% of the protein families found within a genome appear to be common to all kingdoms of life. Recent analyses of completed genomes, exploiting the structural data, have revealed the extent to which duplication of these domains and modifications of their functions can expand the functional repertoire of the organism, contributing to increasing complexity.

MetaRouter: bioinformatics for bioremediation

Bioremediation, the exploitation of biological catalysts (mostly microorganisms) for removing pollutants from the environment, requires the integration of huge amounts of data from different sources. We have developed MetaRouter, a system for maintaining heterogeneous information related to bioremediation in a framework that allows its query, administration and mining (application of methods for extracting new knowledge). MetaRouter is an application intended for laboratories working in biodegradation and bioremediation, which need to maintain and consult public and private data, linked internally and with external databases, and to extract new information from it. Among the data-mining features is a program included for locating biodegradative pathways for chemical compounds according to a given set of constraints and requirements. The integration of biodegradation information with the corresponding protein and genome data provides a suitable framework for studying the global properties of the bioremediation network. The system can be accessed and administrated through a web interface. The full-featured system (except administration facilities) is freely available at http://pdg.cnb.uam.es/MetaRouter. Additional material: http://www.pdg.cnb.uam.es/biodeg_net/MetaRouter.

Variation and evolution of biomolecular systems: Searching for functional relevance

The availability of genome sequences and functional genomics data from multiple species enables us to compare the composition of biomolecular systems like biochemical pathways and protein complexes between species. Here, we review small- and large-scale, "genomics-based" approaches to biomolecular systems variation. In general, caution is required when comparing the results of bioinformatics analyses of genomes or of functional genomics data between species. Limitations to the sensitivity of sequence analysis tools and the noisy nature of genomics data tend to lead to systematic overestimates of the amount of variation. Nevertheless, the results from detailed manual analyses, and of large-scale analyses that filter out systematic biases, point to a large amount of variation in the composition of biomolecular systems. Such observations challenge our understanding of the function of the systems and their individual components and can potentially facilitate the identification and functional characterization of sub-systems within a system. Mapping the inter-species variation of complex biomolecular systems on a phylogenetic species tree allows one to reconstruct their evolution.

Network analysis of metabolic enzyme evolution in Escherichia coli

BACKGROUND

The two most common models for the evolution of metabolism are the patchwork evolution model, where enzymes are thought to diverge from broad to narrow substrate specificity, and the retrograde evolution model, according to which enzymes evolve in response to substrate depletion. Analysis of the distribution of homologous enzyme pairs in the metabolic network can shed light on the respective importance of the two models. We here investigate the evolution of the metabolism in E. coli viewed as a single network using EcoCyc.

RESULTS

Sequence comparison between all enzyme pairs was performed and the minimal path length (MPL) between all enzyme pairs was determined. We find a strong over-representation of homologous enzymes at MPL 1. We show that the functionally similar and functionally undetermined enzyme pairs are responsible for most of the over-representation of homologous enzyme pairs at MPL 1.

CONCLUSIONS

The retrograde evolution model predicts that homologous enzymes pairs are at short metabolic distances from each other. In general agreement with previous studies we find that homologous enzymes occur close to each other in the network more often than expected by chance, which lends some support to the retrograde evolution model. However, we show that the homologous enzyme pairs which may have evolved through retrograde evolution, namely the pairs that are functionally dissimilar, show a weaker over-representation at MPL 1 than the functionally similar enzyme pairs. Our study indicates that, while the retrograde evolution model may have played a small part, the patchwork evolution model is the predominant process of metabolic enzyme evolution.

Enzyme classification by ligand binding

The problem of assigning a biochemical function to newly discovered proteins has been traditionally approached by expert enzymological analysis, sequence analysis, and structural modeling. In recent years, the appearance of databases containing protein-ligand interaction data for large numbers of protein classes and chemical compounds have provided new ways of investigating proteins for which the biochemical function is not completely understood. In this work, we introduce a method that utilizes ligand-binding data for functional classification of enzymes. The method makes use of the existing Enzyme Commission (EC) classification scheme and the data on interactions of small molecules with enzymes from the BRENDA database. A set of ligands that binds to an enzyme with unknown biochemical function serves as a query to search a protein-ligand interaction database for enzyme classes that are known to interact with a similar set of ligands. These classes provide hypotheses of the query enzyme's function and complement other computational annotations that take advantage of sequence and structural information. Similarity between sets of ligands is computed using point set similarity measures based upon similarity between individual compounds. We present the statistics of classification of the enzymes in the database by a cross-validation procedure and illustrate the application of the method on several examples.

The organization of the microbial biodegradation network from a systems-biology perspective

Microbial biodegradation of environmental pollutants is a field of growing importance because of its potential use in bioremediation and biocatalysis. We have studied the characteristics of the global biodegradation network that is brought about by all the known chemical reactions that are implicated in this process, regardless of their microbial hosts. This combination produces an efficient and integrated suprametabolism, with properties similar to those that define metabolic networks in single organisms. The characteristics of this network support an evolutionary scenario in which the reactions evolved outwards from the central metabolism. The properties of the global biodegradation network have implications for predicting the fate of current and future environmental pollutants.

A perspective on enzyme catalysis

The seminal hypotheses proposed over the years for enzymatic catalysis are scrutinized. The historical record is explored from both biochemical and theoretical perspectives. Particular attention is given to the impact of molecular motions within the protein on the enzyme's catalytic properties. A case study for the enzyme dihydrofolate reductase provides evidence for coupled networks of predominantly conserved residues that influence the protein structure and motion. Such coupled networks have important implications for the origin and evolution of enzymes, as well as for protein engineering.

Evolution of the protein repertoire

Most proteins have been formed by gene duplication, recombination, and divergence. Proteins of known structure can be matched to about 50% of genome sequences, and these data provide a quantitative description and can suggest hypotheses about the origins of these processes.

A structural perspective on genome evolution

Protein translations of over 100 complete genomes are now available. About half of these sequences can be provided with structural annotation, thereby enabling some profound insights into protein and pathway evolution. Whereas the major domain structure families are common to all kingdoms of life, these are combined in different ways in multidomain proteins to give various domain architectures that are specific to kingdoms or individual genomes, and contribute to the diverse phenotypes observed. These data argue for more targets in structural genomics initiatives and particularly for the selection of different domain architectures to gain better insights into protein functions.

Metabolites: a helping hand for pathway evolution?

The evolution of enzymes and pathways is under debate. Recent studies show that recruitment of single enzymes from different pathways could be the driving force for pathway evolution. Other mechanisms of evolution, such as pathway duplication, enzyme specialization, de novo invention of pathways or retro-evolution of pathways, appear to be less abundant. Twenty percent of enzyme superfamilies are quite variable, not only in changing reaction chemistry or metabolite type but in changing both at the same time. These variable superfamilies account for nearly half of all known reactions. The most frequently occurring metabolites provide a helping hand for such changes because they can be accommodated by many enzyme superfamilies. Thus, a picture is emerging in which new pathways are evolving from central metabolites by preference, thereby keeping the overall topology of the metabolic network.